Optimal Inventory Management Research Articles

ANALISIS BIAYA PENJUALAN MENGGUNAKAN PERHITUNGAN POQ DALAM MERENCANAKAN DAN MENGENDALIKAN PERSEDIAAN POMPA AIR SUBMERSIBLE DI TOKO. XYZ

Optimal inventory management is one of the key elements in supply chain management, especially for companies focusing on products with fluctuating demand, such as submersible water pumps. This study aims to analyze the planning and control of submersible water pump inventory at PT. XYZ using the Period Order Quantity (POQ) method. The POQ method was chosen for its ability to determine the optimal order quantity based on actual demand periods, thereby minimizing inventory costs, including ordering and holding costs. The analysis process begins by identifying historical demand data, inventory costs, and other relevant parameters. Subsequently, the POQ method is applied to calculate the optimal order quantity for each period based on demand levels and the ordering cycle time. The study's findings indicate that implementing the POQ method can reduce total inventory costs compared to conventional methods employed by the company. Moreover, this method enhances inventory management efficiency by reducing ordering frequency and the risk of stockouts. In conclusion, the POQ method has proven effective in helping PT. XYZ manage submersible water pump inventory more efficiently and economically. The study recommends developing an integrated inventory information system to support the sustainable implementation of this method.

Research on the Optimization of Inventory Management Through Radio Frequency Identification Technology

In the era of rapid development of information technology, inventory management, as a core component of enterprise operations, directly relates to cost control and market response speed. However, traditional inventory management methods have many limitations, such as delayed information updates and inaccurate inventory data. These issues severely affect the operational efficiency and market competitiveness of enterprises. Therefore, exploring new technological means to optimize inventory management has become an urgent need for innovative management in businesses. Radio Frequency Identification (RFID) technology, as an advanced automatic identification technology, has been widely applied in various logistics fields. As an essential component of the logistics supply chain, researching the application of RFID technology in inventory management to reduce costs and increase efficiency holds innovative and strategic significance. This study takes RFID technology as an entry point to explore its application potential and optimization strategies in inventory management. RFID technology, with its rapid and accurate data reading capability, provides a new solution for inventory management.

Optimal InventoryManagement for Deteriorating and Ameliorating Items with Stochastic Demand and Time-varying Holding Cost.

The present paper deals with an optimal inventory management for deteriorating and ameliorating items with stochasticdemand in which shortages are allowed withfully backlogged. The holding cost follows time dependent. Here deterioration is timevarying and the environment of amelioration followedby a two-parameter Weibull (Swedish engineer Wallodi Weibull) distribution to describe the different life spans effectively by utilizing the changes of the parameters. The demand pattern is assumed to be linearly dependent on to time with a stochastic error. The model is minimized to the total average cost by finding optimal values. The developed model is illustrated by a numerical example and finally the sensitivity analysis for the optimal solutions towards the changes in the values of key parameters has been presented.

Bi-Level Inventory Formalization

A bi-level problem is defined for the optimization of inventory management. This formalization allows the simultaneous delivery of different types of goods according to the required volume of consumption for the production of goods. The resources, which are supplied are strictly related to the production volumes, which minimize the inventory costs. The bi-level optimization problem is defined for the case of delivery of food for diet preparations with appropriate content. The costs for delivery are minimized for a given nutrition content. The empirical comparisons and results of the bi-level inventory management give advantages for the derived optimization problem.

Research on supply chain efficiency optimization algorithm based on reinforcement learning

Supply chain efficiency is critical to enterprises and can affect their competitiveness. The supply chain faces an uncertain and complex external market environment, facing the problem of supply chain efficiency optimization; the traditional optimization method is ineffective, which can better face the current environment and deal with problems. It has advantages in optimizing supply chain efficiency and has been widely used. This paper first expounds on the importance of supply chain management status, the limitations of traditional supply chain management methods, and reinforcement learning in the application of supply chain optimization. Then, through experiments, reinforcement learning, supply chain optimization problems, and the analysis of related algorithm design, the optimal algorithm focuses on inventory management optimization. Finally, this paper points out the future research directions and development trend of the supply chain efficiency optimization algorithm based on reinforcement learning.

Bayesian Sequential Learning and Decision Making in Bike‐Sharing Systems

ABSTRACTIn this article, we introduce modeling strategies for sequentially learning various types of demand uncertainty in bike‐share networks and propose methods for optimal station inventory management. Our approach is motivated by a real bike‐share network in Seoul, South Korea, with 40,000 bikes over a network of 2500 stations covering 25 municipal districts. In doing so, we consider novel Bayesian state space models that are suitable for fast and efficient learning of dynamically evolving system parameters for both intra‐day and inter‐week planning horizons. Our proposed approach provides an overall solution for operation managers where sequential parameter updating, demand prediction, and inventory decision making are addressed simultaneously and is straightforward to implement for the end‐user. We illustrate how our approach can be applied to a large metropolitan area like Seoul and discuss practical implementation insights.

Optimization of inventory management through computer vision and machine learning technologies

Optimization of inventory management through computer vision and machine learning technologies

Enhancing Inventory Management through Safety-Stock Strategies—A Case Study

Efficient inventory management, including optimal safety-stock levels, is crucial for operational continuity and cost-effectiveness in various industries. This study seeks the optimal inventory management strategy to minimize costs and determine ideal safety-stock levels. It compares five approaches: the company’s (STAR) current “number of days” method, two alternative models from the literature (the theory of constraints (TOC) replenishment model and the service-level approach), and two newly developed hybrid methodologies (the TOC replenishment model with ABC–XYZ classification and the service-level approach with ABC–XYZ classification). The analysis focused on financial performance, considering inventory holding and shortage costs. Monthly production plans were established and fixed as constant based on predetermined optimum month-end inventory levels derived from each method. Through simulation, actual month-end inventory levels were assessed, comparing total inventory costs (TICs). While unit holding costs (UHCs) were documented in financial records in the company, unit shortage costs (USCs) were not; thus, USCs were examined in three scenarios. The results show that the second proposed hybrid model consistently outperformed the other four methods, including the company’s current approach, significantly reducing TIC. The analysis emphasizes the importance of demand variation in setting safety stocks and demonstrates the second hybrid methodology’s effectiveness in optimizing safety-stock strategies and improving overall inventory management efficiency.

The Role and Impact of Artificial Intelligence on Supply Chain Management: Efficiency, Challenges, and Strategic Implementation

This research paper delves into the transformative impact of artificial intelligence (AI) on supply chain management, focusing on enhancing demand forecasting, operational efficiency, and customer satisfaction, while also managing costs and streamlining logistics operations. The adoption of AI in supply chains offers significant opportunities to improve service delivery and operational capabilities, which are crucial for maintaining competitiveness in the rapidly evolving business landscape. The study underscores the importance of AI technologies in reshaping supply chain dynamics by providing a comprehensive analysis of both the benefits and challenges associated with its implementation. Key benefits highlighted include the optimization of inventory management, enhanced accuracy of demand forecasting, reduced operational costs, and improved customer service. These enhancements are pivotal in achieving a competitive edge and adapting to changing market demands.However, the integration of AI into supply chains is not devoid of challenges. The paper identifies critical obstacles such as the need for significant cultural shifts within organizations, data security concerns, and the complexities of navigating legal and regulatory frameworks. These challenges require strategic management to ensure successful AI adoption and to mitigate associated risks.The research includes case studies of Arab companies that have integrated AI into their supply chains, offering practical insights into the real-world application of these technologies. These examples demonstrate both the potential rewards and the difficulties encountered, providing a balanced perspective on the practicalities of AI deployment in supply chain settings.In conclusion, while AI presents substantial opportunities for advancing supply chain management, it also necessitates careful consideration of various implementation challenges. The paper provides strategic recommendations for Arab companies aiming to leverage AI technologies effectively. These guidelines emphasize the need for thorough planning, continuous risk assessment, and fostering an adaptive organizational culture. By addressing these key areas, companies can harness the full potential of AI to enhance their supply chain operations and achieve sustainable growth.

Inventory Management Optimization: Balancing Cost and Service Levels

Efficient management of inventory plays a role, in the success of businesses across sectors. Finding the equilibrium between keeping inventory costs low and meeting customer needs is a challenge that requires constant exploration and creativity. This study delves into the complexities of optimizing inventory management focusing on strategies that effectively handle the trade offs between minimizing expenses and fulfilling customer expectations. By delving into an analysis of existing literature this study consolidates approaches, models and best practices implemented by companies to enhance the efficiency of inventory management. It thoroughly examines aspects such as improving demand prediction accuracy reducing lead times optimizing safety stock levels and integrating supply chains. Additionally it explores how advanced technologies like intelligence data analytics and automation are transforming inventory management processes.Moreover this research delves into how industry landscapes, market dynamics and business goals influence inventory management tactics. Through real world examples and empirical data analysis it offers insights, on how organizations can tailor their strategies to meet needs while balancing the objectives of cost control and service level improvement. In the end this study adds to what we know about improving inventory management by providing advice and tips for companies to gain lasting edges in todays ever changing market. KEYWORDS: Research Paper, Blockchain, Management, Risk Evaluation, Empirical Analysis

Revolutionizing Supply Chain Management: Real-time Data Processing and Concurrency

In the contemporary business landscape, effective supply chain management (SCM) is paramount for organizations seeking to thrive amidst evolving market dynamics and heightened customer expectations. This research paper presents a pioneering approach to SCM that harnesses cutting-edge technologies, namely Kafka and Akka, to revolutionize data integration and decision-making processes. By leveraging Kafka as a robust distributed event streaming platform and Akka as a versatile toolkit for developing concurrent and distributed applications, our system facilitates seamless communication and coordination across diverse nodes within the supply chain network. This paper elucidates the intricacies of the proposed architecture, detailing the implementation methodology and performance evaluation metrics. Through a comprehensive examination, we demonstrate how our solution enhances supply chain visibility, fosters operational agility, and enables real-time responsiveness to market fluctuations and customer demands. Moreover, practical use cases exemplify the transformative impact of our approach on inventory management optimization, order fulfillment efficiency, and logistics optimization. Furthermore, we delve into the challenges encountered during implementation and deployment, offering insights into potential mitigative strategies. Finally, we outline avenues for future research, exploring emerging trends and opportunities in the realm of SCM empowered by Kafka and Akka technologies.

Implementation of the First In First Out (FIFO) Algorithm in the Sandal and Shoe Product Inventory (Stock) Application

This study addresses the optimization of inventory management for sandal and shoe products, at CV Diva Karya Mandiri Warehouse, which covers of five key features: a dashboard, master data management, transaction data, reporting, and user management. The First In First Out (FIFO) algorithm is specifically applied to the transaction feature, ensuring timely disbursement in line with the order of receipt. It is implemented using Rapid Application Development (RAD) methodology, which consists of Planning Requirements, User Design, Construction, and Cutover phases. The developed inventory application offers two access levels: administrators with comprehensive access and warehouse managers with limited access for viewing, searching, and filtering item data. This study successfully implementing the FIFO algorithm, with 95% Blackbox testing result achieved through boundary value analysis approach.Top of Form

Enhancing Economic Development through Inventory Management Optimization in Agro-based Industries in Bihar: A Comparative Study of EOQ and EPQ Models

Inventory management is crucial for optimizing operational efficiency and cost-effectiveness in agro-based industries, particularly in regions like Bihar,known for their agricultural resources. This study compares the effectiveness of two inventory management models, the Economic Order Quantity (EOQ) and Economic Production Quantity (EPQ), in addressing the unique challenges faced by Bihar's agro-based industries. Data from Taruwar Agro Industries Private Limited in Ashiyana, Patna, use to calculate EOQ and EPQ values for three product categories: Tomato Chili, Peri Peri, and Punjabi Tadka. The study employs relevant formulas to compute optimal order quantities and total inventory costs, assuming constant demand, quality, price, holding costs, and ordering costs. Both the EOQ and EPQ models offered viable approaches to inventory optimization. EOQ focused on minimizing holding and ordering costs, while EPQ integrated production-related costs to optimize production flow. The study underscored the importance of aligning inventory management strategies with operational capabilities, market demand, and financial objectives. This study contributes to advancing theoretical understanding and practical applications of inventory management in agro-based economies like Bihar. Recommendations for empirical validation, contextual adaptation, technology integration, and long-term impact assessment are provided to enhance inventory management practices. Integration of the BCG matrix into decision-making processes offers a comprehensive approach to prioritizing inventory management strategies and guiding strategic implications.

Machine Learning Prediction Techniques in the Optimization of Diagnostic Laboratories’ Network Operations

The article presents an outline of the concept of a prototype system allowing for the optimization of inventory management in a diagnostic laboratory on the basis of patients results. The effectiveness of laboratory diagnostics depends largely on the appropriate management of resources and the quality of tests. A functional quality management system is an integral element of every diagnostic laboratory, ensuring reliability and appropriate work standards. This system includes maintaining correct and reliable analytical test results as well as the optimal use of the laboratory equipment’s processing capacity and the appropriate organization of the supply chain—both analytical material and reagents. It is extremely important to avoid situations in which tests cannot be performed due to a lack of reagents, the overloading of analyzers, or improper calibration. Therefore, the accurate prediction of the number of orders is crucial to optimize the laboratory’s operations, both in the short term—for the next few hours and minutes—and in the longer term, even monthly, which will allow for the appropriate planning of reagent stock. As part of the research presented in this article, machine learning methods were used to implement the above functionalities, which allowed for the development of a prototype of a laboratory optimization system using patient test results as a basis.

Tata Kelola Persediaan Bahan Baku Usaha Djamoe Van Java untuk Meningkatkan Efisiensi Pembuatan Jamu

Raw material inventory is unprocessed basic material provided by the company for later use in the company's production process. Raw materials are important in production companies. Limited or excess supplies of raw materials can be a problem for the company. A shortage of raw materials can result in problems with company activities, especially in the production process, while excess supplies of raw materials can lead to excessive costs in maintaining raw materials and the value of raw materials will decrease over time (Anista & Widiyastuti, 2016). The method used in this research is quantitative descriptive. According to Winartha (2006: 155), the quantitative descriptive method is analyzing, describing and summarizing various conditions and situations from various data in the form of numbers collected from the results of analysis and interviews or observations regarding the problems being studied that occur in the field. Based on the calculation results, the optimal quantity of raw material orders for the Djamoe Van Java business is 10 Kg. Based on the results of the research and discussion described previously, it can be concluded that to obtain optimal raw material inventory management for the Djamoe Van Java business, the quantity The economic purchase (EOQ) that the company must provide is 10 kg. The results of calculations that have been carried out in Total Inventory Cost (TIC) show that after implementing an appropriate and optimal inventory control system, the company can save costs of IDR 150,000. Companies should determine Reorder Points in carrying out raw material inventory control to anticipate possible delays in ordering raw materials.

Forecasting superstore fruit and vegetable sales based on ARIMA-LSTM

In the context of a fresh food supermarket, managing perishable vegetables, which have a notably limited shelf life, maintaining optimal stock levels becomes imperative for safeguarding profitability. This research paper intricately explores the analysis of numerous datasets using Power Query and Power BI, with a pivotal focus on devising a succinct coding system. To ensure data quality, outliers are meticulously eliminated using advanced techniques like linear interpolation and Z-Score processing. The ARIMA time series model is employed to predict the sales volume for each category in July, having been trained on data from June 2022 to June 2023, adeptly capturing historical sales patterns and emerging trends. Additionally, a parameter-tuned LSTM recurrent neural network, optimized with Adam's algorithm, is utilized to refine predictions, considering its inherent memory capabilities. A thoughtfully designed weighted average approach combines outputs from both models, strategically minimizing overfitting and enhancing prediction accuracy. The resultant research fortifies inventory management optimization, bolstering the supermarket's profitability while effectively meeting customer demands.

Advanced supply chain coordination for efficient project execution in oil & gas projects

Effective project execution in the oil and gas industry depends heavily on robust supply chain coordination. Advanced supply chain coordination strategies are crucial for addressing the complex, multi-faceted logistics and procurement challenges typical of oil and gas projects, which often operate in remote and geographically challenging environments. This review explores the role of advanced coordination techniques in enhancing the efficiency of supply chains, improving project timelines, reducing costs, and mitigating risks. The integration of digital technologies such as Internet of Things (IoT), artificial intelligence (AI), and blockchain is transforming supply chain coordination by enabling real-time monitoring, predictive analytics, and secure data sharing among stakeholders. These technologies facilitate better communication and collaboration across the supply chain, ensuring that materials and resources are delivered on time and within budget. The optimization of inventory management, transportation logistics, and supplier relationships through these technologies enhances operational efficiency, minimizes delays, and reduces operational risks associated with supply disruptions. Furthermore, the use of advanced planning and scheduling tools enables oil and gas companies to synchronize the activities of various project stakeholders, ensuring that construction, engineering, and procurement teams are aligned with the overall project objectives. By incorporating predictive modeling and data-driven decision-making, companies can anticipate potential bottlenecks and take proactive measures to resolve them before they affect project timelines or costs. In conclusion, advanced supply chain coordination strategies are essential for ensuring efficient project execution in the oil and gas sector. By leveraging cutting-edge technologies and data analytics, companies can improve project performance, optimize resource utilization, and reduce risks. This approach ultimately contributes to the timely delivery of projects, ensuring that they are completed within budget and to the required standards of quality and safety.

Artificial Intelligence and Business Models from the Perspective of Innovation and Operational Efficiency of Companies: Systematic Literature Review

The application of Artificial Intelligence (AI) in various business models has grown rapidly as the need to improve efficiency, better decision-making, and a superior customer experience has grown. The study maps AI adoption trends across various industries, identifies the most impacted sectors, and highlights the growing reliance on AI in decision-making and automation. The financial, manufacturing, e-commerce, and healthcare sectors are areas that are showing significant transformation through the use of AI. In finance, AI is used for risk analysis and fraud detection, while in manufacturing, AI supports process automation and predictive maintenance, which increases productivity and keeps operational costs down. In e-commerce, AI enables personalization of shopping experiences and optimization of inventory management, and in healthcare, AI supports the analysis of patient data for better outcomes. AI-based digital transformation is also changing the company’s strategy and operational structure. Companies now rely more on responsive and flexible organizational design, which allows for more efficient allocation of resources. In addition, AI is driving innovation through the development of economic platforms, personalization, and service automation. However, challenges remain, especially when it comes to change management and employee skill development for new technology adaptation. Thus, AI not only improves efficiency and innovation in business models, but also requires effective change management strategies so that companies can compete in a dynamic business environment.

Improvement of Inventory Management and Demand Forecasting by Big Data Analytics in Supply Chain

Abstract Inventory management plays a very important role in the process of business operation, providing favorable backing for the smooth operation of production and sales. In this paper, LightGBM and PSO-LSTM models in big data technology are combined to improve inventory management and demand forecasting in supply chains. Then, the relationship between inventory, order, and forecast is elaborated, the two-level inventory cost components and the relationship between them are analyzed, the model constraints are formulated, and a mathematical model for two-level multi-cycle inventory control is constructed. Finally, the single demand forecasting model is compared with the improved model to explore the optimization effect of inventory management after the application of the LightGBM-PSO-LSTM model. The LightGBMPSO-LSTM model is the best fit and can be used for actual demand forecasting. After the optimization of inventory management, the inventory turnover ratio of Company H increased from 8.2 in 2022 to the maximum value of 9.2 in 2023, and the OTIF achievement rate of sales orders increased from 97.9% in 2022 to 99.3% in 2023. This paper provides a successful example of optimizing supply chain inventory management using big data analytics.

Enhancing Inventory Management through Advanced Technologies and Mathematical Methods: Utilizing Neutrosophic Fuzzy Logic

Optimal inventory management is one of the most critical components for companies to thrive in the competitive market while meeting their customers’ demands, reducing costs, and developing their operations. In this paper, the utilization of different technologies and instruments ranging from the most modern ones to mathematical ones was analyzed to demonstrate how the system can function successfully. It is expected that Neutrosophic fuzzy logic is one of the most complicated approaches that allow for proper uncertainty management, forecasting, and inventory control improvements. Fundamentally, the process could be that much more insightful due to the availability of mathematical modelling and on-the-go support systems. Through the use of dynamic programming with the help of Python tools to process these models, Full optimization under fuzzy demand is possible to achieve. Therefore, one could conclude that companies have many opportunities to develop their operations, reduce costs, and keep their customers happy even in a highly dynamic and uncertain business environment.